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A fast algorithm for mining high average-utility itemsets

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Abstract

Mining high-utility itemsets (HUIs) in transactional databases has become a very popular research topic in recent years. A popular variation of the problem of HUI mining is to discover high average-utility itemsets (HAUIs), where an alternative measure called the average-utility is used to evaluate the utility of itemsets by considering their lengths. Albeit, HAUI mining has been studied extensively, current algorithms often consume a large amount of memory and have long execution times, due to the large search space and the usage of loose upper bounds to estimate the average-utilities of itemsets. In this paper, we present a more efficient algorithm for HAUI mining, which includes three pruning strategies to provide a tighter upper bound on the average-utilities of itemsets, and thus reduce the search space more effectively to decrease the runtime. The first pruning strategy utilizes relationships between item pairs to reduce the search space for itemsets containing three or more items. The second pruning strategy provides a tighter upper bound on the average-utilities of itemsets to prune unpromising candidates early. The third strategy reduces the time for constructing the average-utility-list structures for itemsets, which is used to calculate their upper bounds. Substantial experiments conducted on both real-life and synthetic datasets show that the proposed algorithm with three pruning strategies can efficiently and effectively reduce the search space for mining HAUIs, when compared to the state-of-the-art algorithms, in terms of runtime, number of candidates, memory usage, performance of the pruning strategies and scalability.

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Acknowledgment

This research was partially supported by the National Natural Science Foundation of China (NSFC) under grant No.61503092 and by the CCF-Tencent IAGR20160115.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jerry Chun-Wei Lin & Shifeng Ren

  2. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Philippe Fournier-Viger

  3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Department of Information Management, Cheng Shiu University, Kaohsiung, Taiwan

    Ja-Hwung Su

  6. Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh, Vietnam

    Bay Vo

  7. College of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea

    Bay Vo

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  1. Jerry Chun-Wei Lin
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  2. Shifeng Ren
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  3. Philippe Fournier-Viger
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  4. Tzung-Pei Hong
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  5. Ja-Hwung Su
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  6. Bay Vo
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Correspondence to Jerry Chun-Wei Lin.

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Lin, J.CW., Ren, S., Fournier-Viger, P. et al. A fast algorithm for mining high average-utility itemsets. Appl Intell 47, 331–346 (2017). https://doi.org/10.1007/s10489-017-0896-1

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  • DOI: https://doi.org/10.1007/s10489-017-0896-1

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